تقرير
A Mathematical Theory of Attention
العنوان: | A Mathematical Theory of Attention |
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المؤلفون: | Vuckovic, James, Baratin, Aristide, Combes, Remi Tachet des |
سنة النشر: | 2020 |
المجموعة: | Computer Science Statistics |
مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
الوصف: | Attention is a powerful component of modern neural networks across a wide variety of domains. However, despite its ubiquity in machine learning, there is a gap in our understanding of attention from a theoretical point of view. We propose a framework to fill this gap by building a mathematically equivalent model of attention using measure theory. With this model, we are able to interpret self-attention as a system of self-interacting particles, we shed light on self-attention from a maximum entropy perspective, and we show that attention is actually Lipschitz-continuous (with an appropriate metric) under suitable assumptions. We then apply these insights to the problem of mis-specified input data; infinitely-deep, weight-sharing self-attention networks; and more general Lipschitz estimates for a specific type of attention studied in concurrent work. |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/2007.02876 |
رقم الأكسشن: | edsarx.2007.02876 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |